Gladys Morrison

921 total citations
10 papers, 570 citations indexed

About

Gladys Morrison is a scholar working on Oncology, Molecular Biology and Cellular and Molecular Neuroscience. According to data from OpenAlex, Gladys Morrison has authored 10 papers receiving a total of 570 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Oncology, 3 papers in Molecular Biology and 2 papers in Cellular and Molecular Neuroscience. Recurrent topics in Gladys Morrison's work include Cancer Treatment and Pharmacology (3 papers), Pain Mechanisms and Treatments (2 papers) and Neuroscience and Neuropharmacology Research (2 papers). Gladys Morrison is often cited by papers focused on Cancer Treatment and Pharmacology (3 papers), Pain Mechanisms and Treatments (2 papers) and Neuroscience and Neuropharmacology Research (2 papers). Gladys Morrison collaborates with scholars based in United States, Ireland and United Kingdom. Gladys Morrison's co-authors include M. Eileen Dolan, Jamie R. Brewer, Gini F. Fleming, Xiaoyong Fu, Rachel Schiff, Mothaffar F. Rimawi, C. Kent Osborne, Gail D. Lewis Phillips, Gary C. Chamness and Nuala Healy and has published in prestigious journals such as SHILAP Revista de lepidopterología, Genome Research and Oncotarget.

In The Last Decade

Gladys Morrison

10 papers receiving 557 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Gladys Morrison United States 6 371 202 101 100 81 10 570
Charlotta All‐Ericsson Sweden 18 379 1.0× 480 2.4× 106 1.0× 122 1.2× 108 1.3× 25 1.0k
Patricia B. Trusk United States 8 450 1.2× 374 1.9× 114 1.1× 134 1.3× 120 1.5× 13 786
Mary Guckert United States 13 462 1.2× 509 2.5× 76 0.8× 29 0.3× 93 1.1× 18 808
Andrea Gombos Belgium 15 529 1.4× 303 1.5× 239 2.4× 82 0.8× 210 2.6× 40 805
Olivia Gardner United States 13 407 1.1× 669 3.3× 113 1.1× 17 0.2× 102 1.3× 22 929
Jodi L. Kroeger United States 12 510 1.4× 191 0.9× 83 0.8× 18 0.2× 51 0.6× 17 720
Christine Pellegrino United States 10 230 0.6× 261 1.3× 59 0.6× 16 0.2× 62 0.8× 13 448
Kai Hauser Germany 10 186 0.5× 323 1.6× 177 1.8× 159 1.6× 161 2.0× 29 759
M. Lafitte France 14 146 0.4× 401 2.0× 65 0.6× 105 1.1× 146 1.8× 28 723
Woo-Hee Jung South Korea 14 207 0.6× 298 1.5× 62 0.6× 53 0.5× 377 4.7× 22 648

Countries citing papers authored by Gladys Morrison

Since Specialization
Citations

This map shows the geographic impact of Gladys Morrison's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Gladys Morrison with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gladys Morrison more than expected).

Fields of papers citing papers by Gladys Morrison

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gladys Morrison. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Gladys Morrison. The network helps show where Gladys Morrison may publish in the future.

Co-authorship network of co-authors of Gladys Morrison

This figure shows the co-authorship network connecting the top 25 collaborators of Gladys Morrison. A scholar is included among the top collaborators of Gladys Morrison based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Gladys Morrison. Gladys Morrison is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

10 of 10 papers shown
1.
Gruener, Robert F., Alexander Ling, Ya-Fang Chang, et al.. (2021). Facilitating Drug Discovery in Breast Cancer by Virtually Screening Patients Using In Vitro Drug Response Modeling. Cancers. 13(4). 885–885. 10 indexed citations
3.
Morrison, Gladys, Marina N. Asiedu, Nayereh Ghoreishi‐Haack, et al.. (2019). The NMDAR modulator NYX-2925 alleviates neuropathic pain via a Src-dependent mechanism in the mPFC. SHILAP Revista de lepidopterología. 7. 100039–100039. 4 indexed citations
4.
Geeleher, Paul, Zhenyu Zhang, Fan Wang, et al.. (2017). Discovering novel pharmacogenomic biomarkers by imputing drug response in cancer patients from large genomics studies. Genome Research. 27(10). 1743–1751. 88 indexed citations
5.
Wang, Fan, Jeremy Chang, Zhenyu Zhang, et al.. (2017). Discovering drugs to overcome chemoresistance in ovarian cancers based on the cancer genome atlas tumor transcriptome profile. Oncotarget. 8(70). 115102–115113. 1 indexed citations
6.
Morrison, Gladys, Bonnie LaCroix, Dana Ziliak, et al.. (2016). Utility of patient-derived lymphoblastoid cell lines as an ex vivo capecitabine sensitivity prediction model for breast cancer patients. Oncotarget. 7(25). 38359–38366. 2 indexed citations
7.
Morrison, Gladys, Cong Liu, Claudia Wing, et al.. (2015). Evaluation of inter-batch differences in stem-cell derived neurons. Stem Cell Research. 16(1). 140–148. 10 indexed citations
8.
Brewer, Jamie R., Gladys Morrison, M. Eileen Dolan, & Gini F. Fleming. (2015). Chemotherapy-induced peripheral neuropathy: Current status and progress. Gynecologic Oncology. 140(1). 176–183. 199 indexed citations
9.
Morrison, Gladys, Xiaoyong Fu, Martin J. Shea, et al.. (2014). Therapeutic potential of the dual EGFR/HER2 inhibitor AZD8931 in circumventing endocrine resistance. Breast Cancer Research and Treatment. 144(2). 263–272. 39 indexed citations
10.
Wang, Yen-Chao, Gladys Morrison, Ryan Gillihan, et al.. (2011). Different mechanisms for resistance to trastuzumab versus lapatinib in HER2- positive breast cancers - role of estrogen receptor and HER2 reactivation. Breast Cancer Research. 13(6). R121–R121. 213 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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